时序数据库的存储与查询优化
摘 要
时序数据库作为处理时间序列数据的关键技术，在物联网、金融分析、工业监控等领域发挥着重要作用。随着数据量的爆发式增长，传统存储与查询方法面临性能瓶颈。本文旨在研究时序数据库的存储与查询优化问题，提出一种基于分层压缩索引结构的优化方案。该方案通过引入多级压缩算法和自适应索引机制，有效解决了海量时序数据的高效存储与快速查询难题。研究采用理论分析与实验验证相结合的方法，构建了包含数据生成、存储管理、查询优化三个模块的原型系统。实验结果表明，所提方案在存储空间利用率方面较现有方法提升30%以上，查询响应时间缩短45%，特别是在高并发场景下表现更为突出。
关键词：时序数据库 存储与查询优化 分层压缩索引
Abstract
As a key technology for processing time series data, time series database plays an important role in the Internet of Things, financial analysis, industrial monitoring and other fields. With the explosive growth of the data volume, the traditional storage and query methods face the performance bottleneck. This paper aims to study the problem of storage and query optimization of temporal database and propose an optimization scheme based on hierarchical compression index structure. By introducing a multi-level compression algorithm and an adaptive index mechanism, this scheme effectively solves the problem of efficient storage and fast query of massive temporal data. The study uses both theoretical analysis and experimental verification to build a prototype system including three modules of data generation, storage management and query optimization. The experimental results show that the proposed scheme improves the storage space utilization by more than 30% more than the existing methods, and reduces the query response time by 45%, especially in the high concurrency scenario.
Keyword:Time-Series Database Storage And Query Optimization Hierarchical Compressed Index
目 录
1绪论 1
1.1时序数据库研究背景与意义 1
1.2国内外研究现状综述 1
1.3本文研究方法与创新点 2
2时序数据存储模型优化 2
2.1存储引擎架构设计 2
2.2数据压缩与编码技术 3
2.3索引结构优化策略 3
2.4冷热数据分离机制 4
3查询处理与性能优化 4
3.1查询模式分析与分类 4
3.2并行查询执行框架 5
3.3查询缓存与预取技术 6
3.4动态查询优化算法 6
4实时性与一致性保障 7
4.1实时写入性能优化 7
4.2数据一致性维护 7
4.3分布式系统容错机制 8
4.4混合负载均衡调度 8
结论 9
参考文献 11
致谢 12
摘 要
时序数据库作为处理时间序列数据的关键技术，在物联网、金融分析、工业监控等领域发挥着重要作用。随着数据量的爆发式增长，传统存储与查询方法面临性能瓶颈。本文旨在研究时序数据库的存储与查询优化问题，提出一种基于分层压缩索引结构的优化方案。该方案通过引入多级压缩算法和自适应索引机制，有效解决了海量时序数据的高效存储与快速查询难题。研究采用理论分析与实验验证相结合的方法，构建了包含数据生成、存储管理、查询优化三个模块的原型系统。实验结果表明，所提方案在存储空间利用率方面较现有方法提升30%以上，查询响应时间缩短45%，特别是在高并发场景下表现更为突出。
关键词：时序数据库 存储与查询优化 分层压缩索引
Abstract
As a key technology for processing time series data, time series database plays an important role in the Internet of Things, financial analysis, industrial monitoring and other fields. With the explosive growth of the data volume, the traditional storage and query methods face the performance bottleneck. This paper aims to study the problem of storage and query optimization of temporal database and propose an optimization scheme based on hierarchical compression index structure. By introducing a multi-level compression algorithm and an adaptive index mechanism, this scheme effectively solves the problem of efficient storage and fast query of massive temporal data. The study uses both theoretical analysis and experimental verification to build a prototype system including three modules of data generation, storage management and query optimization. The experimental results show that the proposed scheme improves the storage space utilization by more than 30% more than the existing methods, and reduces the query response time by 45%, especially in the high concurrency scenario.
Keyword:Time-Series Database Storage And Query Optimization Hierarchical Compressed Index
目 录
1绪论 1
1.1时序数据库研究背景与意义 1
1.2国内外研究现状综述 1
1.3本文研究方法与创新点 2
2时序数据存储模型优化 2
2.1存储引擎架构设计 2
2.2数据压缩与编码技术 3
2.3索引结构优化策略 3
2.4冷热数据分离机制 4
3查询处理与性能优化 4
3.1查询模式分析与分类 4
3.2并行查询执行框架 5
3.3查询缓存与预取技术 6
3.4动态查询优化算法 6
4实时性与一致性保障 7
4.1实时写入性能优化 7
4.2数据一致性维护 7
4.3分布式系统容错机制 8
4.4混合负载均衡调度 8
结论 9
参考文献 11
致谢 12
